Influence of Normative Theories of Ethics on the European Union Artificial Intelligence Act: A Transformer-Based Analysis Using Semantic Textual Similarity

Abstract: This study investigates the ethical grounding of the European Union Artificial Intelligence (EU AI) Act by using Semantic Textual Similarity (STS) to analyze the alignment between normative ethical theories and regulatory language. Despite being regarded as a significant step toward regulating AI systems and its emphasis on fundamental rights, the EU AI Act is not immune to moral criticism regarding its ethical foundations. Our work examines the impact of three major normative theories of ethics, virtue ethics, deontological ethics, and consequentialism, on the EU AI Act. We introduce the concept of influence, grounded in philosophical and chronological analysis, to examine the underlying relationship between the theories and the Act. As a proxy measure of this influence, we propose using STS to quantify the degree of alignment between the theories (influencers) and the Act (influencee). To capture intentional and operational ethical consistency, the Act was divided into two parts: the preamble and the statutory provisions. The textual descriptions of the theories were manually preprocessed to reduce semantic overlap and ensure a distinct representation of each theory. A heterogeneous embedding-level ensemble approach was employed, using five modified Bidirectional Encoder Representations from Transformers (BERT) models built on the Transformer architecture to compute STS scores. These scores reflect the semantic alignment between various theories of ethics and the two components of the EU AI Act. The resulting similarity scores were evaluated using voting and averaging, with findings indicating that deontological ethics has the most significant overall influence.